public SolutionInstance GetBestNeighbors()
{
SolutionInstance best = Neighbors.OrderBy(c => c.Cost).First();
if (best.Cost < Cost)
{
return(best.GetBestNeighbors());
}
return(this);
}
internal SolutionInstance DoCreateInstance(IReadOnlyList <int> coords)
{
var s = CreateInstance(coords);
if (_bestEver == null || _bestEver.Cost > s.Cost)
{
_bestEver = s;
}
return(s);
}
public SolutionInstance GetRandomInstance()
{
SolutionInstance s = CreateInstance(Dimensions.Select(c => _random.Next(c)).ToArray()).GetBestNeighbors();
if (_bestResult == null || s.Cost < _bestResult.Cost)
{
_bestResult = s;
}
return(s);
}
public SolutionInstance CreateInstance(IReadOnlyList <int> coordinates)
{
var s = DoCreateInstance(coordinates);
if (_theBest == null || s.Cost < _theBest.Cost)
{
_theBest = s;
}
return(s);
}
public override IEnumerable <SolutionInstance> GeneratePopulation(IEnumerable <SolutionInstance> previousPopulation, int populationSize)
{
List <SolutionInstance> newPopulation = new List <SolutionInstance>();
if (previousPopulation == null)
{
for (int i = 0; i < populationSize; i++)
{
SolutionInstance randomInstance = _space.GetRandomInstance();
newPopulation.Add(randomInstance);
}
return(newPopulation);
}
// Arbitrary number
int breakingIndex = _space.Random.Next(1, _space.Dimensions.Count() - 1);
int breedingsDone;
if (_introduceDiversity && ComputeDiversity(previousPopulation) < _diversityThreshold)
{
breedingsDone = (int)Math.Round((populationSize / 2 * (1 - _diversityFactor)));
}
else
{
breedingsDone = populationSize / 2;
}
// Each crossover brings two new children
for (int i = 0; i < breedingsDone; i++)
{
SolutionInstance[] parents = previousPopulation.Skip(_space.Random.Next(previousPopulation.Count() - 1)).Take(2).ToArray();
SolutionInstance father = parents[0];
SolutionInstance mother = parents[1];
int[] childOneCoordinates = father.Coordinates.Take(breakingIndex).Concat(mother.Coordinates.Skip(breakingIndex).Take(_space.Dimensions.Count() - breakingIndex)).ToArray();
int[] childTwoCoordinates = mother.Coordinates.Take(breakingIndex).Concat(father.Coordinates.Skip(breakingIndex).Take(_space.Dimensions.Count() - breakingIndex)).ToArray();
SolutionInstance childOne = _space.CreateInstance(childOneCoordinates);
SolutionInstance childTwo = _space.CreateInstance(childTwoCoordinates);
newPopulation.Add(childOne);
newPopulation.Add(childTwo);
}
int newPopulationSize = newPopulation.Count();
while (newPopulationSize < populationSize)
{
newPopulation.Add(_space.GetRandomInstance());
newPopulationSize++;
}
return(newPopulation);
}
public static SolutionInstance Best(this IEnumerable <SolutionInstance> candidates)
{
SolutionInstance best = null;
foreach (var s in candidates)
{
if (best == null || s.Cost < best.Cost)
{
best = s;
}
}
return(best);
}
/// <summary>
/// Solves the problem associated with the solution space.
/// </summary>
/// <returns></returns>
public SolutionInstance Solve(Func <SolutionInstance, IEnumerable <SolutionInstance>, bool> endCondition, int populationSizePerIteration, int maxIterations)
{
SolutionInstance best = null;
IEnumerable <SolutionInstance> population = null;
int currentIteration = 0;
while (!endCondition(best, population) && currentIteration < maxIterations)
{
population = GeneratePopulation(population, populationSizePerIteration);
population = FindBestIndividualsInPopulation(population);
best = _space.TheBest;
currentIteration++;
}
return(best);
}
public override IEnumerable <SolutionInstance> FindBestIndividualsInPopulation(IEnumerable <SolutionInstance> population)
{
double threshold = population.Average((individual) => individual.Cost);
return(population.Select((individual) => {
SolutionInstance bestNeighbor = individual.BestAmongNeigbors();
if (bestNeighbor.Cost < individual.Cost)
{
return bestNeighbor;
}
else
{
// Instead of only returning the individual, let's create a whole new individual to give us more diversity
return _space.GetRandomInstance();
}
}).Where((individual) => individual.Cost <= threshold).ToArray());
}
public SolutionInstance CrossWith(SolutionInstance mom)
{
return(null);
}